A graph can give fraud-investigation agents the connected context that isolated account scores miss: which accounts used the same devices, which identities link to multiple transactions, and how those relationships connect to counterparties or prior cases. TigerGraph describes several ways to provide agents with graph context, but its published material does not document a specific system built with 11 agents. This article therefore presents a proposed 11-agent design, not a report of a verified deployment or measured result.
Why investigate fraud as a graph?
Fraud often appears as a pattern across entities rather than a single suspicious record. An account may look ordinary by itself but become more concerning when it shares a device with several other accounts, connects to a reused identity, or participates in transactions involving the same counterparty.
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A graph represents entities as vertices and their relationships as edges. A fraud graph might include customers, accounts, transactions, devices, identities, phone numbers, addresses, merchants, claims, and risk signals. A query can traverse those connections to retrieve a connected path or neighborhood for an investigator to examine. TigerGraph’s fraud materials illustrate this approach with a healthcare scenario connecting a provider, treatment center, administrators, addresses, and patient claims; its example includes an eight-hop query. That hop count belongs to the illustration, not a general requirement for fraud investigations.
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What graph retrieval adds to an agent
An agent that can retrieve connected graph context can ask questions such as “What other accounts used this device?” or “How are these two claims connected?” The graph returns entity relationships and paths; the agent can then summarize them, identify follow-up questions, or request another retrieval. Access should be limited to defined queries and permissions rather than giving an agent unrestricted control over the database.
Graph retrieval and vector search serve different purposes. Vector search locates semantically similar text, which can help find relevant policies, investigator notes, or case documents. Graph traversal follows explicit links among entities, events, and operational state. TigerGraph’s Victor Lee writes that “Graph retrieval should complement, not replace, vector search” in the August 18, 2026 article Agentic AI Architecture: How Graph Databases Power Intelligent Agent Systems. A system can use both: retrieve relationship evidence from the graph and relevant passages from document search, then present the sources distinctly in the agent’s working context.
A proposed roster of 11 agents
The following is a design proposal for dividing a complex investigation into bounded tasks. TigerGraph’s published material describes general single-agent, multi-agent, GraphRAG, and MCP patterns; it does not establish this roster, its prompts, or a tested orchestration. In a real deployment, agent responsibilities should reflect the institution’s data, policies, and investigator workflow.
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Case intake agent
Turns an alert or investigator request into a structured case question, recording the subject entities, time window, and requested outcome without deciding whether fraud occurred.
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Entity resolution agent
Finds candidate matches among identities, accounts, devices, addresses, and counterparties, while retaining match confidence and distinguishing confirmed links from uncertain ones.
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Graph retrieval agent
Runs approved graph queries for relevant neighbors and paths, returning the entities, relationship types, timestamps, and query context that support each finding.
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Transaction pattern agent
Examines transaction sequences and linked counterparties within the authorized scope, flagging patterns for further checking rather than labeling them conclusive.
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Device and identity agent
Reviews device, identity, contact, and address connections, with care not to treat a shared attribute as proof of common control.
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Network expansion agent
Explores whether a potentially relevant cluster connects to additional accounts or entities, applying limits on traversal depth, result size, and scope.
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Document retrieval agent
Uses vector search or another approved document retrieval method to find pertinent policies, case notes, or supporting materials, returning passages with their source identifiers.
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Risk and policy agent
Checks retrieved facts against the organization’s documented risk indicators and applicable investigation policy. It should explain which rule or indicator applies instead of inventing a rationale.
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Counter-evidence agent
Looks for plausible benign explanations, conflicting records, missing links, and alternative interpretations that could weaken the initial suspicion.
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Case synthesis agent
Combines findings into a concise account of the evidence, separating graph facts, document excerpts, model-generated interpretations, and unresolved questions.
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Quality and audit agent
Checks that claims in the draft trace to retrieved evidence, that permissions and required review steps were followed, and that the investigation record captures the relevant queries and outputs.
These agents need not be eleven separate models or services. They can be logical roles implemented by one model with distinct tools, by several agents coordinated through a workflow, or by a hybrid system. Splitting roles is useful only when it improves control, specialization, or review enough to justify the added orchestration and monitoring.
Choose an integration pattern that fits the workflow
TigerGraph describes four relevant patterns: a single agent that calls graph retrieval, multiple agents using shared graph memory, an iterative GraphRAG retrieval loop, and an agent connected through MCP for live graph access. They are architectural options rather than evidence that a particular 11-agent system has been built or evaluated.
| Pattern | Useful when | Shared context and freshness | Trade-offs to plan for |
|---|---|---|---|
| Single agent with a graph retrieval tool | The investigation is bounded and one agent can handle the reasoning and retrieval sequence. | The agent maintains its task context; each graph call can retrieve current information, subject to the data pipeline’s freshness. | Fewer orchestration components, but tool permissions, query limits, and evidence citations still need controls. It may be harder to separate independent review tasks. |
| Multiple agents with shared graph memory | Different tasks can be performed in parallel or require distinct specialist roles. | Agents can refer to common graph-backed context, but the design must define what is shared, how updates are represented, and whether agents see the same version of evidence. | Coordination, conflicting outputs, access control, and shared-state management add complexity. Shared memory should not become an unreviewed channel for one agent’s conclusions to appear as facts. |
| Iterative GraphRAG | The question requires successive retrieval: an initial result informs a narrower or deeper follow-up query. | Each iteration can add graph evidence to the working context; fresh results depend on when the underlying data was updated. | Retrieval loops need stopping conditions, traversal bounds, and safeguards against repeatedly amplifying an early mistaken match. |
| MCP-connected graph access | Agents need a standardized way to invoke live graph tools through an MCP-compatible interface. | Tool calls can access current graph state as exposed by the connected service; shared context depends on the agent workflow, not MCP alone. | The MCP server and its tool surface require authentication, authorization, input validation, logging, and operational ownership. A connection standard does not itself guarantee safe or correct queries. |
For a first implementation, a single agent with tightly scoped graph retrieval is often the clearest baseline to evaluate. Add separate roles, shared state, or iterative retrieval only when a real task requires them. TigerGraph identifies task complexity, number of agents, data freshness, and shared context as factors in choosing an architecture; governance, auditability, latency, and operational effort are also practical design considerations, not comparative test results from the vendor material.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to build the investigation flow
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Define the case question and decision boundary
Specify what the system is allowed to do: for example, assemble evidence for an investigator, not freeze an account or make a final fraud determination. Set the permitted subject, time range, data classes, and escalation path before adding agent tools.
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Model entities and relationships
Define graph types for the entities and events that matter, then define edge types with clear meanings. A relationship such as “used device” needs provenance and time context; it should not silently imply common ownership. Record source system, observation time, and confidence where the data supports them.
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Establish identity and data-quality rules
Decide how records are matched, how uncertain matches are represented, and how stale or conflicting data is handled. Entity resolution errors can create misleading clusters, so the graph should preserve the distinction between an observed link and a resolved identity claim.
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Expose bounded retrieval tools
Provide a narrow set of approved graph queries rather than unrestricted query construction. Set limits for traversal depth, result count, time range, and sensitive fields. Return evidence with stable entity identifiers, relationship labels, timestamps, and source provenance so another component or investigator can check it.
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Connect document retrieval where needed
Index relevant unstructured material separately for semantic retrieval. Keep document passages and graph relationships identifiable as different evidence types, and preserve document source and location so a reviewer can inspect the original.
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Orchestrate agents with explicit handoffs
Define which role can call which tool, what input it receives, and what structured output it must return. A specialist should return findings and evidence references, not an opaque confidence score. The synthesis step should distinguish retrieved facts from generated interpretation and unresolved questions.
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Require investigator review and preserve an audit trail
Record the case scope, tool calls, graph query parameters, retrieved evidence identifiers, model outputs, and reviewer actions according to organizational policy. Keep consequential decisions with authorized people and existing processes unless a separately validated policy explicitly allows automation.
What can make the system misleading
- Bad or incomplete data: Missing events, outdated records, or inconsistent identifiers can make a real relationship invisible or create a false one.
- Overconfident entity matching: A shared address, device, or phone number can have an innocent explanation. Preserve match uncertainty and examine alternatives.
- Unbounded graph expansion: Following too many hops can flood the case with weak associations. Set task-specific limits and make the path from the subject to each finding inspectable.
- Agent-to-agent contamination: An early agent’s speculation can be repeated as if it were a verified fact. Require evidence references and label hypotheses explicitly.
- Stale or inconsistent context: A graph result and a document result may reflect different update times. Show timestamps and resolve conflicts rather than silently merging them.
- Excessive access: Agents should receive only the data and tools needed for their task. Apply authorization at the retrieval layer, not just in prompts.
- Automation bias: A polished narrative can appear more certain than the underlying evidence. Present supporting paths, counter-evidence, and unknowns alongside summaries.
How to evaluate the design
Before production use, test the system against representative investigations with outcomes reviewed by qualified investigators. Measure whether it retrieves relevant relationships, omits important evidence, introduces unsupported claims, respects access boundaries, and produces reproducible case records. Include benign cases and ambiguous matches, not only clear fraud examples. Compare the multi-agent design with a simpler single-agent baseline; if the added agents do not improve useful evidence retrieval or review quality enough to justify their operational burden, retain the simpler workflow.
Vendor-reported outcome figures should not be treated as expected results for a new implementation. TigerGraph’s webinar page presents claims including faster case resolution and fraud savings, but the underlying methodology was not established in the available material. Those claims do not substitute for an evaluation on the organization’s own data and workflow.
What the 11-agent title does—and does not—establish
The eleven roles above are a proposed decomposition of an investigation workflow. TigerGraph’s cited architecture materials support the broader patterns of graph retrieval, shared graph memory, GraphRAG, and MCP-connected access, but do not document this roster, an implementation using these exact roles, or measured results for such a system. A real case study would need to identify its actual agents, orchestration, graph schema, deployment and governance choices, and evaluation method before making those claims.
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